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检索条件"主题词=Backpropagation Algorithms"

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New accelerated learning algorithm to reduce the oscillation of weights in multilayered neural networks

New accelerated learning algorithm to reduce the oscillation...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： K. Ochiai N. Toda S. Usui Department of Information & Computer Sciences Toyohashi University of Technology Toyohashi Japan

An accelerated learning algorithm is proposed. It is based on R.A. Jacob's heuristics with reduction of the oscillation by correcting the next point of weight to the bottom of a ravine. The performance is evaluated through several simulation examples and compared with several acceleration algorithms, such as the DBD (delta-bar-delta) algorithm and the EDBD (extended delta-bar-delta) algorithm. Simulation results showed that the proposed algorithm converges 3.7 times faster than the DBD algorithm, 1.8 times faster than the EDBD algorithm, and 19.4 times faster than the basic backpropagation.< >

关键词： Acceleration Neural networks Multi-layer neural network backpropagation algorithms Convergence Jacobian matrices Intelligent networks Computer networks Computer errors Kalman filters

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Learning in connectionist networks using the Alopex algorithm

Learning in connectionist networks using the Alopex algorith...

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International Joint Conference on Neural Networks (IJCNN)

作者： K.P. Unnikrishnan K.P. Venugopal Computer Science Department GM Research Laboratories Warren MI USA Center for Advanced Marine Systems Florida Atlantic University Boca Raton FL USA

The Alopex algorithm is described as a universal learning algorithm. The algorithm is stochastic and it can be used for learning in networks of any topology, including those with feedback. The neurons could contain any transfer function and the learning could involve minimization of any error measure. The efficacy of the algorithm is investigated by applying it on multilayer perceptrons to solve problems such as XOR, parity, and encoder. These results are compared with results obtained using a backpropagation learning algorithm. Taking the specific case of the XOR problem, it is shown that a smoother error surface with fewer local minima could be obtained by using an information-theoretic error measure. An appropriate 'annealing' scheme for the algorithm is described, and it is shown that Alopex can escape out of the local minima.< >

关键词： Intelligent networks Neurons backpropagation algorithms Stochastic processes Neural networks Neurofeedback Multilayer perceptrons Integrated circuit interconnections Computer science Laboratories

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Decoupled recursive estimation training and trainable degree of feedforward neural networks

Decoupled recursive estimation training and trainable degree...

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International Joint Conference on Neural Networks (IJCNN)

作者： L. Jin P.N. Nikiforuk M.M. Gupta College of Engineering Intelligent Systems Research Laboratory University of Saskatchewan Saskatoon SAS Canada

A recursive estimation training algorithm of multilayered neural networks (MNNs) with feedforward connections is proposed. The sequential neuron-decoupled extended Kalman filter equations or SNDEKF formulations are derived based on the extended Kalman filter (EKF) algorithm. Except for the advantage of fast convergence, the computational and storage requirements of SNDEKF training are significantly less than those of EKF, and the SNDEKF can naturally be integrated into the parallel structure for the weight vector of each neuron of the network like the conventional backpropagation algorithm. The effectiveness of the training approach is shown through a nonlinear function approximation example.< >

关键词： Recursive estimation backpropagation algorithms Neural networks Multi-layer neural network Feedforward neural networks Equations Convergence Computer networks Concurrent computing Neurons

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Dynamic neural networks with the use of divide and conquer

Dynamic neural networks with the use of divide and conquer

引用

International Joint Conference on Neural Networks (IJCNN)

作者： S.G. Romaniuk L.O. Hall Department of Computer Science and Engineering University of South Florida Tampa FL USA

An algorithm called divide and conquer neural networks that creates a feedforward neural network during training based upon the training examples is described. In addition to learning the weights for connections, it learns an architecture that enables it to learn the examples. Training is done on the inputs to one cell at a time with learned weights being frozen. Error is never propagated backwards through a hidden cell. Examples of the algorithm's performance on the exclusive-OR and the Iris plant data, which contain two nonlinearly separable classes, are given. The results show that this algorithm can effectively learn a viable architecture in which training examples may be encoded and generalized for later use in classification.< >

关键词： Neural networks Feedforward neural networks Computer architecture Iris Convergence Computer vision Detectors backpropagation algorithms Computer science Electronic mail

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Recognition of Japanese words by neural networks using vocal tract area

Recognition of Japanese words by neural networks using vocal...

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International Joint Conference on Neural Networks (IJCNN)

作者： H. Kinugasa H. Kamata Y. Ishida Department of Electronics and Communication Meiji University Kawasaki Japan

The authors present a new system for Japanese word recognition by neural networks using the vocal tract area. They present a method by which the vocal tract area is directly estimated from speech waves. The estimation method applies an adaptive inverse filter to the autocorrelation coefficients. A neural network learning algorithm developed by Y. Ishida et al. (1991), which is based on the conjugate gradient method, is used. The speaker-independent word recognition results for a vocabulary of 10 Japanese words demonstrated the effectiveness of the method.< >

关键词： Neural networks Autocorrelation Adaptive filters Speech backpropagation algorithms Production systems Reflection Convolution Gradient methods Vocabulary

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A convergent neural network learning algorithm

A convergent neural network learning algorithm

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International Joint Conference on Neural Networks (IJCNN)

作者： Z. Tang G.J. Koehler Department of Decision and Info Sciences University of Florida Gainesville FL USA

A globally guided backpropagation (GGBP) training algorithm is presented. This algorithm is a modification of the standard backpropagation algorithm. Instead of changing a weight w/sub ij/ according to the partial derivative or error, E, with respect to w/sub ij/, an attempt is made to minimize E in the output space. The change in weights W is computed based on the desired changes in the output O. The new algorithm is an analog to backpropagation with a dynamically adjusted learning rate eta . This learning rate changing scheme avoids the problems associated with a heuristic learning rate adjusting method. Two main advantages of GGBP are fast learning speed and convergence to a global optimal solution.< >

关键词： Neural networks backpropagation algorithms Feedforward neural networks Feedforward systems Computer networks Multi-layer neural network Acceleration Jacobian matrices Gradient methods Costs

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Fast-robot system identification based on neural network models

Fast-robot system identification based on neural network mod...

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IEEE International Symposium on Intelligent Control (ISIC)

作者： S. Khemaissia A.S. Morris Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK

The authors present a system identification scheme and an adaptive control algorithm for a class of nonlinear systems, based on the computational properties of artificial neural network models. An estimation procedure for the link parameters is described in which identification is carried out using the parallel recursive prediction error technique. The algorithm enables the weights in each neuron of the network to be updated in an efficient parallel manner and has better convergence than the classical backpropagation algorithm. The whole of the algorithm can be distributed over a network of parallel processors to achieve impressive speedup. An example is given for the first three links of the Stanford arm to demonstrate the effectiveness of this algorithm for the cases of dense gain matrix and diagonal gain matrix.< >

关键词： System identification Neural networks backpropagation algorithms Artificial neural networks Adaptive control Nonlinear systems Computer networks Recursive estimation Neurons Convergence

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Learning and optimization with cascaded VLSI neural network building-block chips

Learning and optimization with cascaded VLSI neural network ...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： T. Duong S.P. Eberhardt M. Tran T. Daud A.P. Thakoor Center for Space Microelectronics Technology Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA Department of Engineering Swarthmore College Swarthmore PA USA

To demonstrate the versatility of the building-block approach, two neural network applications were implemented on cascaded analog VLSI chips. Weights were implemented using 7-b multiplying digital-to-analog converter synapse circuits, with 31*32 and 32*32 synapses per chip. A novel learning algorithm compatible with analog VLSI was applied to the two-input parity problem. The algorithm combines dynamically evolving architecture with limited gradient-descent backpropagation for efficient and versatile supervised learning. To implement the learning algorithm in hardware, synapse circuits were paralleled for additional quantization levels. The hardware-in-the-loop learning system allocated 2-5 hidden neurons for parity problems. Also, a 7*7 assignment problem was mapped onto a cascaded 64-neuron fully connected feedback network. In 100 randomly selected problems, the network found optimal or good solutions in most cases, with settling times in the range of 7-100 mu s.< >

关键词： Very large scale integration Neural networks backpropagation algorithms Circuits Digital-analog conversion Heuristic algorithms Supervised learning Hardware Quantization Learning systems

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Precision analysis of stochastic pulse encoding algorithms for neural networks

Precision analysis of stochastic pulse encoding algorithms f...

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International Joint Conference on Neural Networks (IJCNN)

作者： H. Eguchi D.G. Stork G. Wolff Ricoh Research and Development Center Yokohama Japan Ricoh California Research Center Menlo Park CA USA

The authors present mathematical results related to recent neural network algorithms employing stochastic pulse encoding. In such algorithms, neural activations and connection weights are encoded as stochastic streams of pulses, where the average density represents the signal or weight value. The authors show the precise form of expected output for two- and three-input neurons, and describe these functions in the limit for a large number of inputs. They address a fundamental limitation inherent in these stochastic techniques: their finite precision. The precision is dependent upon the pulse averaging period-the longer this period (i.e. the larger the number of pulses sampled), the higher the precision. The authors derived exact expressions for the distribution of neural periods as well as a statistical analysis to find the averaging period required for precision of five bits-a resolution determined by others to be necessary for successful implementations of backpropagation. It is found that approximately=1000 pulses are required for 5-b precision. These results reveal fundamental limits in speed and memory requirements for stochastic pulse implementations of neural learning algorithms.< >

关键词： Algorithm design and analysis Stochastic processes Encoding Neural networks Neurons Signal processing algorithms backpropagation algorithms Very large scale integration Statistical analysis Neural network hardware

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A new time-evolving neural network architecture and algorithm for nonlinear system identification using adaptive filtering techniques

A new time-evolving neural network architecture and algorith...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： G. Govind P.A. Ramamoorthy Department of Electrical & Computer Engineering University of Cincinnati Cincinnati OH USA

Concepts from adaptive filtering and some heuristics are used to obtain a fast convergent online neural network especially suited for nonlinear system identification. Rather than training a fixed neural network structure, the algorithm presented allocates nodes when required. This provides for an optimal allocation of hidden nodes in this structure. The results obtained show that the neural network model presented is a viable approach for nonlinear system identification and can be applied to a large class of nonlinear systems. Simulations are provided that show the fast convergence of this neural network structure.< >

关键词： Neural networks Nonlinear systems System identification backpropagation algorithms Multi-layer neural network Parameter estimation Computer architecture Filtering algorithms Adaptive filters Convergence

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